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Record W2008251848 · doi:10.1002/ejlt.201100146

Fatty acids, 4‐desmethylsterols, and triterpene alcohols from Tunisian lentisc (<i>Pistacia lentiscu</i>s) fruits

2012· article· en· W2008251848 on OpenAlexaff
Hajer Trabelsi, Faouzi Sakouhi, Justin B. Renaud, Pierre Villeneuve, Mohamed Larbi Khouja, P. Mayer, Sadok Boukhchina

Bibliographic record

VenueEuropean Journal of Lipid Science and Technology · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant biochemistry and biosynthesis
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsStigmasterolCampesterolChemistryPopulationFatty acidPistacia lentiscusSterolOleic acidFood scienceLinoleic acidPalmitic acidBotanyPistaciaCholesterolBiologyOrganic chemistryBiochemistryChromatographyEcology

Abstract

fetched live from OpenAlex

Abstract A comparative study was performed to determine the fatty acid, 4‐desmethylsterol and triterpenic alcohols compositions of three different Tunisian populations of Pistacia lentiscus fruit Rimel (RM), Korbous (KO), and Tebaba (TB). Fruits are rich in lipids, which varied from 39.37% (KO) to 42.48% (TB) on a dry weight basis. Qualitatively, fatty acid, sterol, mono‐ and dimethylsterol composition is identical for all populations. Oleic acid was the major fatty acid for all samples, accounting from 40.49% in TB population to 50.72% in RM population followed by the palmitic and linoleic acids. Other fatty acids are present at lower levels. Total sterol amount varied from 109.72 mg/100 g of oil (KO) to 434.26 mg/100 g of oil (RM) with an average of 248.74 mg/100 g of oil. The major 4‐desmethylsterol component in all studied Tunisian populations of P. lentiscus oil was ß‐sitosterol followed by campesterol in TB and KO, and by stigmasterol in RM. The amount of total triterpenic alcohols varied from 42.39 mg/100 g of oil in RM population to 70.41 mg/100 g oil in TB population. The quantitative difference in the fatty acids and 4‐desmethylsterols of the different populations studied could be due to the effect of geographic region and soil type.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.229
Teacher spread0.216 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations12
Published2012
Admission routes1
Has abstractyes

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